Ad hoc networks are self-forming networks which can operate in the absence of any fixed infrastructure. An ad hoc network may typically include a number of geographically-distributed, potentially mobile units, sometimes referred to as “nodes”, which are wirelessly connected to each other by one or more links such as, for example, radio frequency communication channels. The nodes can communicate with each other over a wireless channel without the support of an infrastructure-based or wired network.
Links or connections between the nodes in the network can change dynamically in an arbitrary manner as nodes move in and out of, or within the ad hoc network. Because the topology of an ad hoc network can change significantly, techniques are needed which can allow the ad hoc network to dynamically adjust to these changes. Due to the lack of a central server-controller, many network-controlling functions can be distributed among the nodes such that the nodes can self-organize and reconfigure in response to spectrum topology changes.
Most traditional radios have their technical characteristics set at the time of manufacture. More recently, radios have been built to self-adapt to one of several preprogrammed radio frequency (RF) environments that might be encountered. Cognitive radios (“CRs”) go beyond preprogrammed settings to operate both in known and unknown wireless channels.
CRs have emerged on the forefront of communications technology for those seeking radios capable of conducting quality communications over decreasingly-available RF spectrum due to many more users requiring larger amounts of spectrum for wireless voice, video and data. A CR determines where in the spectrum it can transmit and receive and where it can spectrally move to in the event it can no longer utilize frequency channels that it has been using due to poor channel quality or to being preempted by a primary user or higher priority secondary user.
Two very different approaches have arisen to equip advanced, opportunistic radios with the necessary technological core: geo-location and spectrum sensing. An opportunistic radio in the spectrum sense is one that will try to utilize any available RF spectrum that it can find currently unoccupied and, if operating in a licensed or government regulated band, has a legal government license to use. Geo-location approaches utilize location information of primary users (e.g., television stations, public safety teams) as provided by GPS, for example, to dictate the actual geographical area where opportunistic radios wanting to conduct communications cannot interfere. The second approach is called spectrum sensing. CRs that employ spectrum sensing technologies listen for or sense currently unoccupied channels to carry the traffic of the CR.
Most modern real world applications require at least three CRs communicating with each other to form a wireless network. A cognitive radio so equipped with the ability to initiate and maintain networked communications with other CRs even as each CR is dynamically adjusting the channel(s) it operates on is referred to as a Cognitive Networking Radio (CNR). CNR in general has to do with the radio being fully aware of: 1) who it is, including all of its characteristics (functionality, physical properties and limitations, etc.); and 2) who the users are and their applications and/or missions. CNR involves the radio not only being fully aware of things, but also having a deep enough understanding of the meaning or context of this information in order to allow it to optimize its performance and functionality to satisfy the requirements of the network, applications and users.
It is well-known today that manufacturing a cognitive radio and manufacturing a cognitive networking radio are two very different things. A cognitive radio may be defined as a wireless network node that changes its transmission and reception configuration to avoid interference signals from other users or devices. The cognitive radio monitors its environment within its allotted frequency bands and changes the frequencies or bands over which it operates based on the accessibility to those frequencies. On the other hand, a CNR performs all the functions of a cognitive radio but it also interacts with the networking-specific components and services (routing, quality of service “QoS”, network management, etc.) of both itself and other nodes.
A mobile ad hoc network (MANET) is characterized by the lack of fixed networking infrastructure such as routers, switches, base stations and mobile switching centers in the traditional cellular sense. User nodes (radios) are in general also routers and vice versa. A MANET node is most often battery limited. Also, a MANET's network topology is usually dynamically changing with nodes coming in and going out of the network and with links being established and broken. A node while technically still within the geographic boundaries of the network, may experience a break off in connections to it because of internal node or link failures.
A fully-connected mesh network is one in which there are at least two paths to each node. Partially-connected mesh networks will have some nodes with only one path to it. “Connected” in this case does not have to be limited to each node's nearest one-hop neighbors. It also allows for nodes to be “connected” via multiple hops to all other nodes in the network. Although often used interchangeably in the art, the present application does not define a MANET and a mesh network as one and the same thing. A MANET involves nodes that form a mesh (partial or full), but also may be in motion and have an ad hoc nature or a deterministic or random basis. Although it may be stretching the tolerance of most network engineers, point-to-point, point-to-multipoint and mesh networks (static or mobile) may be thought of as trivial cases of MANETs. As it is now, Bluetooth scatternets are often referred to as ad hoc networks, but again they are just very trivial cases of MANETs. A more detailed description of MANETs and cross-layer communications in MANETs can be found in different documents made available, for example, by the Ubiquitous Internet Research Group through their website (http://cnd.iit.cnr.it/). One such document is entitled “MOBILEMAN, Architecture, Protocols, and Services”, Deliverable D5, by Marco Conti et al. See:
http://cnd.iit.cnr.it/mobileMAN/deliverables/MobileMAN_Deliverable_D5.pdf
There are challenges to implementing a MANET of CNRs. The following constitute problems that affect a CNR's ability to support cognitive networking. Some of the problems include:
1. Negotiating non-interfering frequency-hopping sequences between adjacent ad hoc associations/nodes (“A/Ns”).
2. Exchanging of whitespace/grayspace information between A/Ns.
3. Reconfiguring A/Ns to take on different roles such as the “hub” (master) or “remote” (slave).
4. Determining what knowledge is needed and how to formulate or express information as knowledge configured into the CNR and flowing through it. This “knowledge” is alternatively known in the art and referred to herein as cognitive knowledge base. This problem has to do with the form of the types of knowledge, such as rules, that govern the base of intelligence associated with the CNR. Knowledge could be downloaded and stored in the CNR upon initialization of the network and during post-initialization operation. Rules could include known or estimated allowed spectrum regions, types of traffic to permit use of the CNR, battery capacity/recharge rate/utilization rate under various types of traffic loading or mobility conditions, etc. Also, real-time event data could be collected and converted to forms such as fuzzy variables to be used in decision making at the individual or association CNR levels. Outputs from the CNR could be in the form of knowledge, and not just data, to be used as knowledge inputs for higher level reasoning processes involving the control and management of the whole network. In addition to rules, this knowledge may include but is not limited to knowledge of incumbent transmitters, such as for example, their location, transmission power, antenna characteristics, etc.
5. How to reason on the knowledge determined. Once the expression of the selected knowledge has been decided and knowledge is being or has been collected, then it is necessary to process that knowledge so that meaningful and correct outputs or decisions are achieved by the CNR. This processing is referred to as “reasoning on the information”. Many reasoning engines exist, such as those used in classical expert systems, neural networks, genetic reasoning or fuzzy logic. For example, fuzzy logic has reasoning engines such as Mamdani.
Therefore, there is a need in the art for a network configurations and nodes that address the different problems associated with the use of CNRs in cognitive networks. The present invention introduces a network approach that makes use of a Dynamic Networking Spectrum Reuse Transceiver (“DNSRT”)—a CNR that resolves the foregoing problems as will be described in the following sections.